Non-invasive Disease Diagnosis Research Articles

Notice of Retraction: Infrared Sensing Based Non-Invasive Initial Diagnosis of Chronic Liver Disease Using Ensemble Learning

The liver is a vital human body organ and its functionality can be degraded by several diseases such as hepatitis, fatty liver disease, and liver cancer and so forth. Hence, the early diagnosis of liver diseases is extremely crucial for saving human lives. With the rapid development of multimedia technology, it is now possible to design and implement a non-invasive system that can chronic liver diseases. For this purpose, machine learning and Artificial Intelligence (AI) have been used within the past few years. In this regard, digital image processing supported by AI methods has been implemented in the diagnosis of diseases that also showed high reliability. Therefore, in this paper, an iris feature-based non-invasive technique is proposed by incorporating a novel machine-learning algorithm. The experimental setup involved data set for the models' training included 879 subjects from Pakistan, of which 453 subjects have chronic liver disease and 426 are healthy. The iris images were collected using an infrared camera that consists of a lens, a thermal sensor and digital electronics processing. The lens focuses on the infrared energy on the sensor, using distinctive forms of features twenty-two physiological and thirty-three iris features. The designed classification model for a non-invasive system combined eleven different classifiers and used cross-validation techniques for comparing the results. The overall performance of the model was analyzed using five parameters: accuracy, precision, F-score, specificity, and sensitivity. The results confirmed that the proposed non-invasive model is capable of predicting chronic liver diseases with 98% of accuracy.

Non-invasive diagnosis of nonalcoholic fatty liver disease in patients with type 2 diabetes.

Liver disease has emerged as a significant cause of death in people with type 2 diabetes (T2D). Due to a common underlying pathogenic mechanism, namely insulin resistance, T2D represents the main risk factor for nonalcoholic fatty liver disease (NAFLD), characterized by a buildup of fat in the liver. Globally, NAFLD is the most common liver disease, affecting a quarter of the general adult population. The development of nonalcoholic steatohepatitis (NASH) signifies an increased risk of liver fibrosis progression that can result in cirrhosis, hepatocellular carcinoma (HCC), and death. Liver fibrosis progression and development of cirrhosis is mostly asymptomatic until complications from decompensated end-stage liver disease arise. Traditionally, liver biopsy is used to diagnose NASH and stage fibrosis, however, it is invasive and costly. Non-invasive diagnostic alternatives include serum biomarkers and imaging techniques. Early identification of advanced liver fibrosis is pivotal to prompt initiation of targeted surveillance, including screening for HCC, as well as providing options for current and investigational therapeutic interventions to reduce fibrosis progression. This review gives an update on non-invasive diagnostic tools for NAFLD and liver fibrosis in the specific context of T2D, providing clinicians a pragmatic diagnostic approach to this frequent comorbidity in diabetes medicine.

Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose

ABSTRACT Introduction This paper describes the research work done toward the development of a breath analyzing electronic nose (e-nose), and the results obtained from testing patients with lung cancer, patients with chronic obstructive pulmonary disease (COPD), and healthy controls. Pulmonary diseases like COPD and lung cancer are detected with MOS sensor array-based e-noses. The e-nose device with the sensor array, data acquisition system, and pattern recognition can detect the variations of volatile organic compounds (VOC) present in the expelled breath of patients and healthy controls. Materials and methods This work presents the e-nose equipment design, study subjects selection, breath sampling procedures, and various data analysis tools. The developed e-nose system is tested in 40 patients with lung cancer, 48 patients with COPD, and 90 healthy controls. Results In differentiating lung cancer and COPD from controls, support vector machine (SVM) with 3-fold cross-validation outperformed all other classifiers with an accuracy of 92.3% in cross-validation. In external validation, the same discrimination was achieved by k-nearest neighbors (k-NN) with 75.0% accuracy. Conclusion The reported results show that the VOC analysis with an e-nose system holds exceptional possibilities in noninvasive disease diagnosis applications.

Theoretical investigations of Tamm plasmon resonance for monitoring of isoprene traces in the exhaled breath: Towards chronic liver fibrosis disease biomarkers

Detection of blood-carried volatile organic compounds (VOCs) existing in the exhaled breath of humans is an attractive research point for noninvasive diagnosis of diseases. In this research, we have introduced a novel design of photonic crystals (PCs) for the detection of isoprene traces in the exhaled breath as a biomarker for liver fibrosis. To the best of our knowledge, this idea has been introduced for the first time. The proposed sensor structure contains a one-dimensional (1D) PCs covered with a cavity layer of air and a thin metallic layer of Au is attached on the top surface. Hence, the proposed sensor is configured as, [prism/Au/air cavity/(GaN/SiO2)10]. The main idea of the detection procedure is based on the changes in the levels of isoprene traces as the dry exhaled breath (DEB) fills the cavity layer. The transfer matrix method and the Drude model are adopted to calculate the numerical simulations and reflection spectra of the design. The essential key for sensing isoprene levels is the resonant optical Tamm plasmon (TP) states within the photonic bandgap. The obtained numerical results are investigated for both high and low concentrations of the isoprene trances. Due to the optimization route, the designed sensor could receive a relatively high sensitivity (S) of 0.323 nm/ppm or 300000 nm/RIU. Also, we have obtained a quality factor of 980 for ppm levels and a pronounced value of 5042 for ppb levels. This technique can be reducing the risk of infection during the taking of blood samples by syringe. Finally, the imposed gas sensor here could be of potential interest for monitoring several diseases based on the type and level of the exogenous VOC occurring in the exhaled breath.

P–343 Identification of circulating leukocyte microRNA–125b and microRNA–142–3p expression profiles in women with endometriosis before surgery and at 1 month after surgery

Abstract Study question To investigate the expression profiles of microRNA–125b and micrpRNA–142–3p in women with endometriosis, compared with controls before surgery and at 1 month after surgery. Summary answer The over-expression of miRNA–125b and miRNA–142–3p may be involved in the aetiopathogenesis of endometriosis which is related to systemic chronic inflammation. What is known already Currently, there is no reliable non-invasive diagnostic biomarker for endometriosis. MicroRNAs (miRs) are small, non-coding RNAs that are involved in the post-transcriptional regulation of gene expression and promising biomarker candidates for noninvasive diagnosis of various diseases. Despite the small number of studies found that miRNA–125b and miRNA–142–3p have been associated with endometriosis, further evidence is therefore required from studies that examine these two miRNAs in diagnosis and even follow-up of women with endometriosis. Owing to endometriosis is a systemic chronic inflammatory disease, investigating these miRs in blood leukocytes of patients with endometriosis may illustrates the molecular mechanism of endometriosis. Study design, size, duration This is a prospective longitudinal study performed between 2018 November and 2021 February. The sample size of 42 individuals of two groups were calculated considering the power analysis (α = 0.05) with Mann-Whitney U test (effect size,d=0.8) and were calculated as 80%. Women with endometriosis (n = 21) and surgically confirmed endometriosis-free women (n = 21) were included in the study. Laparoscopy and/or laparotomy was performed to determine the presence or absence of endometriosis. Participants/materials, setting, methods Women aged 18–50 years were recruited from two tertiary hospital settings. Severity of endometriosis was assessed by the rASRM classification. Using real-time quantitative PCR, miRNA–125b and miRNA–142–3p in leukocyte were analyzed in women with endometriosis before surgery and at 1 month after surgery, compared to controls without endometriosis. The results were calculated as relative quantification values. The presumed targets of these two miRNAs were identified via 3 different target prediction algorithms:TargetScan, miRDB and DIANA-TarBase. Main results and the role of chance There were no demographic discrepancies between groups. The relative expression of miRNA–125b and miRNA–142–3p were significantly higher in women with endometriosis than in control subjects before surgery (p = 0.0001;p=0001) and at 1 month after surgery, respectively (p = 0.0001;p=0001). Despite the relative expression of miRNA–125b and miRNA–142–3p were decreased 1.75- and 2.4-fold, respectively at 1 month after surgery, we observed no significant differences in the relative expression of miRNA–125b and miRNA–142–3p between before surgery and at 1 month after surgery, respectively (p = 0.110; p = 0.910). Bioinformatic analyses of three databases showed that miRNA–142–3p expression levels were found to be closely associated with fifty-seven genes. Among these target genes, CFL2, RGL2, WASL, CRK, BNC2, CLOCK, TGFBR1, CIITA and ZNF217 were found to be associated with endometriosis. Whereas, no target gene were observed to be associated with miRNA–125b expression in common with these three databases. The ROC curves showed that the expression of miRNA–125b and microRNA–142–3p had an area under the ROC curve (AUC) of 0.77 (sensitivity 61.9%, specificity 88.1% (0.0001;95%CI 0.65–0.90)) and AUC of 0.71 (sensitivity 52.4%, specificity 73.8% (p < 0.007;95%CI 0.58–0.84)) before surgery, respectively. Correlational analysis showed a significant positive correlation between miRNA–142–3p and Hemoglobin A1c (Spearman’s correlation, r = 0.507;p=0.019) in the endometriosis group. Limitations, reasons for caution Further studies are needed to examine the expression of these miRs with a long-term follow up in order to increase their usefulness as a predictor in the clinical practice. It is required to identify the expressions levels of predicted target genes which are associated with endometriosis and regulated by miRNA–142–3p. Wider implications of the findings: Findings suggest that the over-expression of miRNA–125b and miRNA–142–3p may be potential mechanisms involved in the etiopathogenesis of endometriosis which is a systemic chronic inflammatory disease. We observed that miRNA–125b may be a more reliable biomarker than miRNA–142–3 for noninvasive diagnosis of endometriosis and even follow-up of women with endometriosis. Trial registration number 118S298

Real-time monitoring of plant stresses via chemiresistive profiling of leaf volatiles by a wearable sensor

<h2>Summary</h2> Determination of plant stresses such as infections by plant pathogens is currently dependent on time-consuming and complicated analytical technologies. Here, we report a leaf-attachable chemiresistive sensor array for real-time fingerprinting of volatile organic compounds (VOCs) that permits noninvasive and early diagnosis of plant diseases, such as late blight caused by <i>Phytophthora infestans</i>. The imperceptible sensor patch integrates an array of graphene-based sensing materials and flexible silver nanowire electrodes on a kirigami-inspired stretchable substrate, which can minimize strain interference. The sensor patch has been mounted on live tomato plants to profile key plant volatiles at low-ppm concentrations with fast response (<20 s). The multiplexed sensor array allows for accurate detection and classification of 13 individual plant volatiles with >97% classification accuracy. The wearable sensor patch was used to diagnose tomato late blight as early as 4 days post inoculation and abiotic stresses such as mechanical damage within 1 h.

Aggregation-induced emission dots assisted non-invasive fluorescence hysterography in near-infrared IIb window

Uterine diseases seriously threaten the physical and mental health of women. The main principle, when clinicians adopt examinations, is to achieve efficient diagnosis without negative effect on the physical function including fertility. Hysterography in near-infrared (NIR) IIb window (1500–1700 nm) presents perceptibly enhanced signal to background ratio (SBR) and higher penetration capability compared with those beyond 1000 nm and 1300 nm, but lays down high requirements for the biosafety of fluorophores at the same time. Assisted by the biologically excretable aggregation-induced emission (AIE) dots, non-invasive NIR-IIb fluorescence hysterography visualized typical Y-shaped uteruses, real-time uterine peristalsis or the uterine lesions (mimetic disease statuses in clinic) in mouse models. Significantly, after intrauterine perfusion, the reproductive capacity was unimpaired via fertility assessment and histological analysis. This work could inspire some new ideas for non-invasive clinical diagnosis of uterine diseases and effectively promote the clinical translation of AIE dots.

YSZ-based acetone sensor using a Cd2SnO4 sensing electrode for exhaled breath detection in medical diagnosis

Breath analysis using simple gas sensors is becoming one of the important methods for non-invasive, painless and early diagnosis of certain diseases. Among them, the acetone sensor plays a crucial role in screening and diagnosis of diabetes by detecting acetone concentration in exhaled breath. Here, a stabilized zirconia (YSZ)-based mixed potential type acetone sensor combining with Cd2SnO4 sensing electrode (SE) is introduced, which is expected to be used for pre-diagnosis of diabetes. The developed sensor can effectively detect acetone in the concentration range of 0.05−200 ppm at an optimal operating temperature of 600 °C, with sentivities of −17.3 mV/ppm to 0.05−2 ppm acetone and −88.6 mV/decade to 5−200 ppm acetone. Both the sensing behavior and electrochemical test confirm that the sensing principle of the sensor follows to the mixed potential mechanism. The excellent repeatability, selectivity and long-term stability of the sensor make it reliable in the actual application. In addition, clinical tests for the sensor toward exhaled breath of healthy people and diabetic patients are proposed and strong correlations have been established among the response values, acetone concentrations and blood ketone concentrations, which fully demonstrates the great potential of the sensor for pre-diagnosis of diabetes.

Characterization and prediction of viral loads of Hepatitis B serum samples by using surface-enhanced Raman spectroscopy (SERS)

BackgroundRaman spectroscopy is a promising technique to analyze the body fluids for the purpose of non-invasive disease diagnosis. ObjectivesTo develop a surface-enhanced Raman spectroscopy (SERS) based method for qualitative and quantitative analysis of hepatitis B viral (HBV) infection from blood serum samples. MethodsClinically diagnosed hepatitis B virus (HBV) infected serum samples of patients of different levels of viral loads have been subjected for SERS analysis in comparison with the healthy ones by using silver nanoparticles (Ag NPs) based SERS substrates. The SERS measurements were performed on blood serum samples of 11 healthy and 32 clinically diagnosed HBV patients of different viral load levels of different exponentials including (101, 102 called as low level), (103, 104 called as medium level) and (105, 108 called as high level). Furthermore, multivariate data analysis techniques, Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were also performed on SERS spectral data. ResultsThe SERS spectral features due to biochemical changes in HBV positive serum samples associated with the increasing viral loads were established which could be employed for HBV diagnostic purpose. PCA was found helpful for the differentiation between SERS spectral data of serum samples of different levels of HBV infection and healthy individuals. PLSR model developed with standard samples of known viral loads for predicting the viral loads of blind/unknown samples with 99% predicted accuracy. ConclusionSERS can be employed for qualitative and quantitative analysis of HBV infection from blood serum samples.

Serum Biomarkers for Noninvasive Diagnosis of Liver Diseases: How Laudable are These Tools?

: Innumerable reasons have been reported that affect and infect the liver and cause liver diseases. The evaluation and follow-up of liver fibrosis and cirrhosis have been traditionally performed by liver biopsy. However, it has become evident that this once defined as “gold-standard” is now not the best method as it involves many limitations. Attempts to reveal non-invasive diagnostic tools have generated serum biomarkers, multiple scores, formulae, and imaging modalities. All are better tolerated, safer, more acceptable to the patient, and are less expensive than a liver biopsy. Biomarkers have various advantages like minimally invasive, easy to apply with great availability and easier reproducibility, useful for monitoring therapy and less expensive. But then, direct biomarkers involved in extracellular matrix turnover need further validation in different geographic population and indirect biomarkers may not predict early pathophysiological changes in liver parenchyma. The accuracy and diagnostic value of most, if not all, of these biomarkers remain controversial. Hence, there is a need for a biomarker that is specific for the liver and can identify the magnitude of the clinical outcome of the disease. : In this review, we discuss the clinical utility, limitations, and development of noninvasive biomarkers in their use as diagnostic and prognostic tests and analyze whether the present known serum biomarkers are laudable and accurate tools for the diagnosis of liver diseases.

Chemoresistive Gas Sensors Based on SnO2 and Sn3O4 Nanobelts to Volatile Organic Compounds Detection: A Comparative Investigation

Introduction Volatile organic compounds (VOCs) are organic chemicals with a relatively high vapor pressure at low temperatures released to the environment from biogenic and anthropogenic sources. Hundreds of VOCs have been identified in the human breath, and many of them act as potential biomarkers for noninvasive diagnosis and monitoring of several diseases [1]. Contrarily, the increasing emission of these hazardous compounds in urban areas, mainly from industry and automotive exhaust systems, has been a critical matter of modern society in terms of safety, environmental pollution, and public health [2].Chemoresistors based on semiconducting metal oxides (SMOx) offer unique opportunities for trace detection of VOCs, considering their compatibility for portable devices, simple design, low-cost, and reduced power consumption [3]. While SnO2 has been the most used material in the active layer of market sensors, n-type Sn3O4 is another possible stoichiometry of tin oxide with emergent interests in the gas sensing field over the last years [4,5]. However, only limited research has reported the response of Sn3O4 nanostructures to VOCs detection.In this work, the sensing properties of single-crystalline Sn3O4 nanobelts towards some relevant volatile organic compounds (ethanol, acetone, and toluene) were investigated comparatively to SnO2 in multiple operating conditions. A comprehensive understanding of the conduction mechanism associated with the performance of the sensors was additionally discussed. Materials and Methods Sn3O4 and SnO2 nanobelts were synthesized in a tube furnace by the carbothermal reduction method following optimized synthesis parameters described in our previous work [4]. The materials were collected from different regions of the growth site and separately dispersed in 1,2-propanediol by sonication. Several drops of the solutions were deposited onto alumina substrates containing interdigitated Pt electrodes on the topside and a Pt heater on the backside. The sensors were dried at 80 °C overnight and then annealed at 350°C for 10 minutes.The devices were inserted into a homemade measurement chamber coupled with a gas mixing system equipped with mass-flow controllers. Several concentrations of the target gases, including ethanol, acetone, and toluene, were introduced into the chamber, and the DC electrical resistances were continually monitored at 200, 250, and 300°C. Synthetic air in dry and humid conditions (5%, 30%, and 70% r.h. at 25°C) was used as the reference gas. A constant gas flow of 200 sccm was maintained during all the experiments. Results and Conclusions The gas sensing measurements performed from 200 to 300°C revealed that the baseline resistance of SnO2 and Sn3O4 sensors changes accordingly with the humidity level in the background. It occurs due to the reducing nature of the water vapor on the surface of tin oxide. After exposure to ethanol, acetone, and toluene, the resistance of both sensors decreases following the gas concentrations. These reducing gas species act as surface donors, which improves the number of free electrons in n-type semiconductors. From the calibration curves, the signals of the SnO2 sensor toward toluene, acetone, and ethanol drop as a result of the increment in the relative humidity over the monitored operating temperatures. The same profile was observed in the Sn3O4 sensors at 200°C and 250°C, but the opposite outcome takes place at 300°C, i.e., higher sensor signals were achieved in humid conditions for the tested VOCs. In addition, the sensors exhibit remarkable selectivity to potential interferents such as H2 and CO in the full range of concentrations and working temperature investigated. Changes in the surface band bending were correlated with electrical resistance data based on a depletion layer model. It allowed obtaining information about the electronic structure as a function of the charge carrier concentration induced by the analytes chemisorption on the sensing layers. To the best of our knowledge, this is the first report on 1D single-crystalline Sn3O4 materials towards VOCs detection, opening up new possibilities for further investigations. References Chan, L.W.; Anahtar, M.N.; Ong, T.H.; Hern, K.E.; Kunz, R.R.; Bhatia, S.N. Engineering synthetic breath biomarkers for respiratory disease. Nat. Nanotechnol. 2020, 15, 792–800. He, C.; Cheng, J.; Zhang, X.; Douthwaite, M.; Pattisson, S.; Hao, Z. Recent advances in the catalytic oxidation of volatile organic compounds: A review based on pollutant sorts and sources. Chem. Rev. 2019, 119, 4471–4568. Lin, T.; Lv, X.; Hu, Z.; Xu, A.; Feng, C. Semiconductor metal oxides as chemoresistive sensors for detecting volatile organic compounds. Sensors (Switzerland) 2019, 19, 233. Suman, P.H.; Longo, E.; Varela, J.A.; Orlandi, M.O. Controlled synthesis of layered Sn3O4 nanobelts by carbothermal reduction method and their gas sensor properties. J. Nanosci. Nanotechnol. 2014, 14, 6662–6668. Suman, P.H.; Felix, A.A.; Tuller, H.L.; Varela, J.A.; Orlandi, M.O. Comparative gas sensor response of SnO2, SnO and Sn3O4 nanobelts to NO2 and potential interferents. Sensors Actuators, B Chem. 2015, 208, 122–127.

Using an RTIL-Based Electrochemical Sensor Platform for Trace Detection of Heptane As Biomarker for Lung Cancer

Lung cancer is one the most common diseases worldwide with more than 1.6 million dead since 2012 [1]. The survival rate for lung cancer is also very low as the disease is generally diagnosed only at an advanced stage when the treatment is not possible as significant damage has been done [2]. Hence, there is a need for an early and timely non-invasive method for the diagnosis of lung cancer. Previous studies have shown the use of endogenously produced volatile organic compounds (VOCs) as target analytes for the detection of lung cancer. Endogenous VOCs are produced in the bloodstream as result of cellular metabolic pathways involved in maintaining homeostasis[3]. Subsequently, the VOCs diffuse into the alveolar air in the blood-gas interface quickly in the lung because of their low solubility in the blood and they are exhaled out of the lung[4]. Therefore, detection of VOC levels in exhaled breath can be used to study the biochemical pathways that have been altered as a result of tumorigenesis. A breathomics device can be developed and used as a tool for non-invasive, sensitive, and specific detection of lung cancer. Moreover, the presence of lung cancer tumor is not masked by other diseases since every disease has its own volatiles fingerprint[5] , thereby making the detection mechanism specific in nature. Electrochemical sensors are gaining widespread interest as they are robust in nature, have faster response time, and specific and sensitive signal response. Electrochemical gas sensors usually involve a two or three electrode setup that causes change in signal as a function of the output current/potential. Electrochemical sensors require an electrode, a transducer, and an electrolyte. A good electrolyte should have high chemical and electrochemical stability, should be stable at elevated temperatures and have high charge transfer ability. Room temperature ionic liquids (RTIL) are being explored as suitable electrolytes for gas sensing applications as they follow all these parameters. There is an increasing interest in the field of non-invasive disease diagnosis, primarily using breath as a sample for monitoring the presence of various metabolites. Breath alkanes such as heptane can be used as a signature target analyte to differentiate the healthy subjects from lung cancer patients. Levels of heptane in breath for a healthy individual are within 50 ppb-200 ppb range. Concentrations greater than 400 ppb are found in breath as a result of altered metabolic pathways. We demonstrate the proof of concept of an electrochemical VOC sensor specific and sensitive for the detection of heptane up to 400 ppb toward developing a handheld breath analyzer for on-site applicability (Scheme 1). We use RTIL as a transducer and the inherent redox properties of these VOCs such as heptane make them a suitable candidate for electrochemical detection. Moreover, RTILs allow easy removal of VOCs by joule heating and prevent fouling of the system, thereby increasing the shelf life of the sensor.For this study, RTIL Triethylsulfonium Bis(trifluoromethanesulfonyl)imide with over 98 percent purity and used as received without any further purification. Trace Source™ disposable permeation tube for Heptane was purchased from KINTEK analytical. Chronoamperometry was used as transduction principle for detection and voltage applied was set within the electrochemical window of the RTIL. An electrochemical sensing platform is developed for the detection of heptane in vapor phase. The sensing platform can be used to monitor the presence of heptane in breath upto 400 ppb (LOD). The sensor showed dose dependent response for the concentrations ranging from 400 ppb to 5 ppm. Scheme 1- Schematic illustration of the sensing strategy used for the development of the electrochemical RTIL-based sensor Figure 1-Chronoamperometry scan was performed at −0.6 V for 60 s RTIL-modified interdigitated electrode (IDE) for the target analytes. Calibrated dose response chronoamperograms were observed for heptane, which varies with concentration. References J.-L. Tan, Z.-X. Yong, C.-K. Liam, J Thorac Dis, 8 (2016) 2772–2783.R.L. Siegel, K.D. Miller, A. Jemal, CA Cancer J Clin, 65 (2015) 5–29.W. Cao, Y. Duan, Clin Chem, 52 (2006) 800–811.A.G. Dent, T.G. Sutedja, P. V Zimmerman, J Thorac Dis, 5 Suppl 5 (2013) S540-50.G. Peng, M. Hakim, Y.Y. Broza, S. Billan, R. Abdah-Bortnyak, A. Kuten, U. Tisch, H. Haick, Br J Cancer, 103 (2010) 542–551. Figure 1

MicroRNA and vascular endothelial growth factor (VEGF) as new useful markers in the diagnosis of benign prostatic hyperplasia in dogs

Numerous specific biomarkers with a prognostic and diagnostic value comparable to histopathological findings are now used for non-invasive diagnosis of prostate diseases in humans. Meanwhile, as far as dogs are concerned, the diagnosis of prostate disorders is based solely on clinical examination and ultrasound (USG). Therefore, the aim of the study was to assess the usefulness of two biomarkers, i.e. miRNA-129 and VEGF for the diagnosis of BPH in dogs. The study involved 40 dogs divided into three groups. Group I (n = 9) comprised healthy dogs up to the age of 5 years, Group II (n = 17) comprised dogs between the ages of 5–10 suffering from BPH as confirmed by the examination and Group III (n = 14) comprising dogs over 10 years of age, which also had BPH confirmed. The results demonstrated that dogs in group II and III exhibited a significant decrease in miRNA expression (P < 0.0001) and a significant increase in serum VEGF levels (P = 0.025) when compared to the dogs in group I. There was also a positive correlation between the prostate size and VEGF level. The findings led to the conclusion that the determination of miRNA-129 and VEGF can significantly contribute to the diagnosis of prostate disorders in dogs.

Rapid noninvasive screening of cerebral ischemia and cerebral infarction based on tear Raman spectroscopy combined with multiple machine learning algorithms

Researchers have established a classification model based on tear Raman spectroscopy combined with machine learning classification algorithms, which realizes rapid noninvasive classification of cerebral infarction and cerebral ischemia, which is of great significance for clinical medical diagnosis. Through spectral data analysis, it is found that there are differences in the content of tyrosine, phenylalanine, and carotenoids in the tears of patients with cerebral ischemia and patients with cerebral infarction. We try to establish a classification model for rapid noninvasive screening of cerebral infarction and cerebral ischemia through these differences. The experiment has four parts, including normalization, data enhancement, feature extraction, and data classification. The researchers combined three feature extraction methods with four machine classification models to build a total of 12 classification models. Integrating 8 classification criteria, the classification accuracy of all models is above 85%, especially PLS-PNN has achieved 100% accuracy and better running time. The experimental results show that tear Raman spectroscopy combined with machine learning classification model has a good effect on the screening of cerebral ischemia and cerebral infarction, which is conducive to the noninvasive and rapid clinical diagnosis of cerebrovascular diseases in the future.

Application of Artificial Intelligence Modeling Technology Based on Multi-Omics in Noninvasive Diagnosis of Inflammatory Bowel Disease.

PurposeInflammatory bowel disease (IBD) is difficult to diagnose and classify. The purpose of this study is to establish an artificial intelligence model based on fecal multi-omics data for multi-classification diagnosis of IBD and its subtypes.Materials and MethodsA total of 299 clinical cohort studies were included in this study, including 86 healthy people, 140 CD patients and 73 UC patients. Based on the idea of hierarchical modeling for different groups, we model the total population and the groups with self-evaluation of “very well” and “slightly below par”, respectively. The original total features were fecal multi-omics data, including metagenomics, metatranscriptomics, proteomics, metabolomics, viromics, faecal calprotectin. The importance, collinearity and other feature engineering methods were used to evaluate the features. Finally, three individualized diagnosis models with less features and high accuracy were obtained.ResultsFirst, we screened 111 features to form the optimal feature set for the total population and established a three-classification individual diagnosis model with AUC of 0.83, which can simultaneously diagnose health, CD and UC. Secondly, according to the hierarchical modeling of the total population, we established two models for population with different self-evaluation. For “very well” population, we screened 59 features and established a three-classification diagnostic model with AUC of 0.85. For the self-evaluation population with “slightly below par”, we finally included 22 features and established a three-classification diagnostic model with AUC of 0.84. Only metabolomics and metatranscriptomics features were included in the optimal feature sets.ConclusionThis study provides a valuable method for high accuracy, noninvasive diagnosis and subtype identification of IBD patients. Researchers can choose biomarkers in different models according to different self-evaluation of patients. Simple noninvasive fecal sampling can be used to detect metabolomics and metatranscriptomics data, thus replacing the tedious and painful clinical colonoscopy and biopsy procedures.

Comparison of resting/postexercise ankle-brachial index and transcutaneous partial pressure of oxygen for noninvasive diagnosis of peripheral artery disease in type 2 diabetes mellitus

Background: Type 2 diabetesType 2 diabetes mellitus (T2DM) is a strong risk factor for peripheral artery disease (PAD) and PAD diagnosis in T2DM may indicate coexisting coronary artery disease as well. Postexercise ankle brachial index (ABI) and transcutaneous partial pressure of oxygen (TcPO2) have not been evaluated for PAD diagnosis among Indian T2DM patients. This study aimed to evaluate the performance of resting + postexercise(R + PE) ABI and R + PE-TcPO2 for PAD diagnosis among T2DM patients at increased PAD risk, using colour duplex ultrasound (CDU) as reference standard. MethodsThis prospectively conducted diagnostic accuracy study involved T2DM patients at increased PAD risk. R-ABI≤0.9 or PE-ABI decline >20% from resting value in those with R-ABI between 0.91 and 1.4, R–TcPO2 <30 mm Hg or PE decline of TcPO2 to <30 mm Hg in those with R–TcPO2 ≥30 mm Hg, CDU showing >50% stenosis or complete occlusion of lower extremity arteries constituted PAD. ResultsAmong 168 patients enrolled, R + PE-ABI diagnosed PAD in 19(11.3%), R + PE-TcPO2 in 61 (36.3%) and 17 (≈10%) had PAD finally confirmed by CDU. Sensitivity, specificity, PPV and NPV of R + PE-ABI for PAD diagnosis were 82.3%, 96.7%, 73.7% and 98% and that of R + PE-TcPO2 were 76.5%, 68.2%, 21.3% and 96.2%, respectively. PE-ABI increased the sensitivity of ABI by ≈ 18% and had 100% PPV for PAD. When both ABI and TcPO2 (R + PE tests) were normal, PAD could be safely excluded in 88% of patients. ConclusionPE-ABI should be routinely employed and TcPO2(R/PE) is unreliable as a stand-alone test for PAD detection among moderate to high risk T2DM patients.

Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome.

The maturing development in artificial intelligence (AI) and genomics has propelled the advances in intestinal diseases including intestinal cancer, inflammatory bowel disease (IBD), and irritable bowel syndrome (IBS). On the other hand, colorectal cancer is the second most deadly and the third most common type of cancer in the world according to GLOBOCAN 2020 data. The mechanisms behind IBD and IBS are still speculative. The conventional methods to identify colorectal cancer, IBD, and IBS are based on endoscopy or colonoscopy to identify lesions. However, it is invasive, demanding, and time-consuming for early-stage intestinal diseases. To address those problems, new strategies based on blood and/or human microbiome in gut, colon, or even feces were developed; those methods took advantage of high-throughput sequencing and machine learning approaches. In this review, we summarize the recent research and methods to diagnose intestinal diseases with machine learning technologies based on cell-free DNA and microbiome data generated by amplicon sequencing or whole-genome sequencing. Those methods play an important role in not only intestinal disease diagnosis but also therapy development in the near future.

ZENose (ZIF-Based Electrochemical Nose) Platform for Noninvasive Ammonia Detection.

Breathomics is a widely emerging tool for noninvasive disease diagnosis and focuses on the detection of various levels of volatile organic compounds and inorganic gases present in human breath. One of the rapid, easy-to-use, and noninvasive detection methods being investigated is a system that can measure exhaled breath ammonia levels and can be correlated to the functional state of protein metabolic pathways and the renal functioning system. In this work, we have demonstrated the development of an electrochemical nose system using ferrocene encapsulated into zeolitic imidazole framework, Fc@ZIF-8, which can be successfully used for the detection of ammonia levels in breath. This is the first report of an electrochemical gas sensor platform that uses a faradaic probe (that is ferrocene) encapsulated into a metal-organic framework cavity used for disease diagnosis by monitoring the levels of the target gas and can be used for breathomics applications. This work demonstrates that low levels of ammonia gas (up to 400 ppb) can be detected with high sensitivity and specificity. The morphological and structural characterization of the novel, synthesized Fc@ZIF-8 nanocomposite has been performed using powder X-ray diffraction, field emission scanning electron microscopy, Fourier transform infrared, ultraviolet-visible spectroscopy, and dynamic light scattering. Electrochemical characterization of the material has been performed using a standard glassy carbon electrode, and further application of the material has been shown using the in-house designed and reported spiral electrochemical notification coupled electrode, used for ammonia gas sensing. Cross-reactivity studies have also been performed to demonstrate sensor specificity toward the target gas. We demonstrate the first of its kind electrochemical bifunctional probe platform that can be used for sensing ammonia levels in breath, with high sensitivity and specificity, due to the hybrid material system-zinc-imidazole framework 8 (having excellent physisorption properties) and ferrocene (acting as a redox mediator). We envision that such a sensing system will allow noninvasive and early diagnosis of chronic kidney disease, thus leading to early treatment and a decrease in the mortality rate.

Advanced Biomimetic Nanomaterials for Non-invasive Disease Diagnosis

In modern society, the incidence of cancer, inflammatory diseases, nervous system diseases, metabolic diseases, and cardiovascular diseases is on the rise. These diseases not only cause physical and mental suffering for patients, but also place an enormous burden on society. Early, non-invasive diagnosis of these diseases can reduce the physical and mental pain of patients and social stress. There is an urgent need for advanced materials and methods for non-invasive disease marker detection, large-scale disease screening, and early diagnosis. Biomimetic medical materials are synthetic materials designed to be biocompatible or biodegradable, then developed for use in the medical industry. In recent years, with the development of nanotechnology, a variety of biomimetic medical materials with advanced properties have been introduced. Biomimetic nanomaterials have made great progress in biosensing, bioimaging, and other fields. The latest advance of biomimetic nanomaterials in disease diagnosis has attracted tremendous interest. However, the application of biomimetic nanomaterials in disease diagnosis has not been reviewed. This review particularly focuses on the potential of biomimetic nanomaterials in non-invasive disease marker detection and disease diagnosis. The first part focuses on the properties and characteristics of different kinds of advanced biomimetic nanomaterials. In the second part, the recent cutting-edge methods using biosensors and bioimaging based on biomimetic nanomaterials for non-invasive disease diagnosis are reviewed. In addition, the existing problems and future development of biomimetic nanomaterials is briefly described in the third part. The application of biomimetic nanomaterials would provide a novel and promising diagnostic method for non-invasive disease marker detection, large-scale clinical screening, and diagnosis, promoting the exploitation of devices with better detection performance and the development of global clinical public health.

Elevation of plasma tRNA fragments as a promising biomarker for liver fibrosis in nonalcoholic fatty liver disease

Fibrotic tissue remodelling in nonalcoholic fatty liver disease (NAFLD) will probably emerge as the leading cause of end-stage liver disease in the coming decades, but the ability to diagnose liver fibrosis in NAFLD patients noninvasively is limited. The abnormal expression of tRNA-derived small RNA (tsRNA) in plasma provides a novel idea for noninvasive diagnosis of various diseases, however, the relationship between tsRNAs and NAFLD is still unknown. Here, we took advantage of small RNA-Seq technology to profile tsRNAs in NAFLD patients and found the ubiquitous presence of hepatic tsRNAs secreted into circulating blood. Verification in a cohort of 114 patients with NAFLD and 42 patients without NAFLD revealed that three tsRNAs (tRF-Val-CAC-005, tiRNA-His-GTG-001, and tRF-Ala-CGC-006) were significantly elevated in the plasma of NAFLD patients, and the expression level are associated with NAFLD activity score (calculated from 0 to 8) and fibrosis stage (scored from 0 to 4). In mouse models, we further found that increased plasma levels of these three tsRNAs were positively correlated with the degree of liver fibrosis. Our study potentially identifies a new class of NAFLD biomarkers and reveal the possible existence of tsRNAs in the blood that can be used to predict fibrogenesis risk in patients diagnosed with NAFLD.